Align AI

alignai

This post is part of the Hacker Noon Shareholder Series , where we interview some of the super-investors who made the site you're on right now possible. George Georgallides is the co-founder of Darwin: an AI-enabled platform that uses machine learning models and aggregates corresponding expert evaluations to assess the overall risk and attractiveness of a startups for both investors and founders. We sat down with Georgallides to learn more about this fascinating application of AI and machine learning. Q1 How exactly does Darwin use A.I. and the wisdom of experts to make financing an innovation less risky and more transparent for both entrepreneurs and investors? Our team spent two years researching the process of making investment decisions at the earliest stage, initially examining groups of experts making those decisions - the status quo - and later various machine learning approaches. What emerged was the combination of experts and AI in a collaborative process on what each does best. Machine learning is great at processing vast amounts of complex data. Experts among other things are great at making intuitive decisions or interpreting data creatively. We call that Hybrid Intelligence. Darwin uses machine learning models and aggregates corresponding expert evaluations to assess the overall risk and attractiveness of a startup. Darwin also actively learns from the evaluations and creates digital twins of each expert to scale the decision-making process further. "For investors, Hybrid Intelligence provides both a de-risking and scalability partner." When using Darwin, founders receive feedback on the overall attractiveness of their idea before going to an investor. The process is dynamic - they can test how changes impact their score as much as they want. In addition, they receive feedback on how to improve and, ultimately, the means to accelerate their funding. For investors, Hybrid Intelligence provides both a de-risking and scalability partner. Darwin enhances investors’ due diligence stack and allows them to evaluate more deal flow in a timely and knowledgeable manner. Q2 Much has been written about just how far AI can go in terms of performing complex decision-making tasks. Darwin is advocating for Hybrid Intelligence as the new frontier for collaboration between humans and AI - can you tell us more about Darwin's application/s of that? The applications for Hybrid Intelligence can certainly go far beyond just the early stage innovation environment and really apply to any complex decision-making tasks where uncertainty is high. Medical imaging, sports predictions and movie production are just some examples. Beyond better decision-making though, we also believe that Hybrid Intelligence is a piece of the puzzle in shaping the future of work. One where A.I is not replacing humans but is integrated with humans. For tasks that human intuition is critical, we can now scale it and digitize it. More importantly, for the first time, through the use of our digital twin technology, humans can monetize their intelligence in a way in which they don't have to actively do the work. They can ‘outsource’ their intelligence and monetize it passively. Overall, we believe that Hybrid Intelligence has the potential to help align AI and human goals. Q3 How is building an AI investment platform different from building other VC companies? There are some similarities in that, VC funds source deals and conduct due diligence in order to find profitable investment opportunities. But the similarities end there. First of all, Darwin doesn’t invest in startups itself. Rather, Darwin is an intelligence partner representing the interests of both founders and investors. Building Darwin involved years of research and data gathering from founders, accelerators, investors and other stakeholders followed by technical execution and ongoing development to build a scalable solution for identifying successful investment opportunities. Second, unlike VCs, Darwin places a big emphasis on helping founders develop their ideas and make them investable whereas VCs mostly work with startups ready to receive funding. Third, Darwin can scale its core competency whereas a VC fund is inherently limited by how many people work directly or indirectly at the fund. Darwin can help remove that bottleneck for VCs. There are certainly many other ways that Darwin, being a platform, differs from a VC, which is a service company. But the important thing to note, as I mentioned, is that Darwin is a complement to the VC process. Q4 has developed a Digital Disruption Design (D3) model that helps corporations, investors and entrepreneurs with concrete strategic decision-making and data-driven decisions - walk us through that framework, and how it’s integrated into Darwin’s process? Darwin We developed the Digital Disruption Design (D3) model with the support of business incubators, accelerators, VCs and corporate innovation arms. We built on the idea of the Lean Startup for iterative development, testing, and learning in order to provide a process that helps entrepreneurs make decisions. The model has been tested in collaboration with the University of St. Gallen with over 200 participating digital ventures. D3 guides innovators through the use of data-driven methods for validating their assumptions and build their ideas. It’s a stage gate process with decision points at the end of each stage. Each stage is an iterative process of making decisions, testing hypothesis, validating assumptions, and learning. It also provides a set of decision possibilities for each dimension and metrics for making the progress measurable. This framework is integrated into the Darwin platform. The D3 model can be applied for a variety of stakeholders, including startups developing their ideas or corporate venturers trying to define innovation pathways. In addition, we believe it can help incubators, accelerators, and investors monitor their portfolio’s progress. Q5 The VC market is still fairly non-inclusive, in terms of who can actively participate. How might Darwin aid in democratizing opportunities on both sides of the fence - for both investors and entrepreneurs? We’ve met with hundreds of investors and entrepreneurs across the world in the past two years. What we’ve learned is that there’s an incredible opportunity to make cross-border and cross-cultural connections for all parties involved. There are many opportunities to leverage hybrid intelligence to integrate otherwise timid early stage investors by equipping them with more confidence on what represents a good investment opportunity by connecting the dots. In addition, there’s a big opportunity to present highly qualified deals from under-represented areas of the US and the world to investors who otherwise would not be exposed to those deals - thereby making the access to “smart money” more widespread. Q5 For entrepreneurs interested in joining Darwin’s innovation platform, what can they expect in terms of investment opportunities, users/customers, mentorship? We want to work with founders as early as idea-stage and help them cultivate their startup all the way through fundraising. First and foremost, we want to help them get feedback on whether their product has product-market fit. We’ve seen too many founders spend months fundraising with no success before they’ve validated if they provide enough value in their market. On Darwin, they can get feedback from investors and other industry experts on their business model, team-solution fit, traction and much more. Darwin also wants to help startups improve their chances of fundraising by providing opportunities to create traction. One way we are doing that is by matching startups with corporations who are willing to be a startup’s first paying customer. In addition, we’re focused on providing value to the startups through a growing network of partner offers. Something really exciting that we are working on bringing to founders very soon, is the ability to get feedback from the digital twin of the investor they want to pitch and adapt the product to make a killer pitch before the real meeting. Overall, Darwin wants to make fundraising and more transparent for founders and help them go to market the right way. Q6 What are the key actions early-stage startup founders should focus on to reduce their risk of failure? Product-market fit and execution top the list in a fairly wide spectrum of variables that can impact early stage success or failure. The biggest challenge we’ve heard, from first-time founders especially, is not knowing how to go about validating their idea and what to focus on with regards to early execution in order to make their venture more attractive to investors and potential users/customers. That’s why we think Darwin fills such a big piece of that puzzle in providing an on-demand tool to get support on both those key areas. Q7 What’s a ‘digital twin’, and how does Darwin use this to help startups succeed in pitching? A digital twin is a digital copy of a user on Darwin. Our machine learning models actively learn from the input of Darwin experts. That means we can employ the knowledge of experts even without their physical input to increase the confidence in the evaluation of a particular idea. Our digital twin feature is still in development as we gather data from those users but when it’s ready to launch, will include VCs, successful entrepreneurs, and other specialists. Q8 For VCs looking for investable startups, what’s to be gained from joining Darwin as an investor? Almost every VC that identifies herself as such will have more deal flow than they can evaluate at present. What we offer is a scalable way to vet not only that deal flow, but also additional opportunities originating from our partners around the world with less time spent on due diligence. This way, VCs are less likely to miss out on an investment opportunity because they “didn’t have enough time”. Furthermore, due to the fact that opportunities on Darwin are evaluated by a diverse brain trust of industry experts and then further evaluated by our AI, VCs can use Darwin to de-risk their own investment decisions. This ensures that they don’t miss out on deals because of cognitive biases or limited knowledge on an opportunity’s addressable market, and at the same time not make bad investments due to overconfidence. "The early stage investment space is such a foundational driver in shaping the future of our world... Darwin is trying to organize the vast number of incredible innovation opportunities that exist, help shape them through the platform and present them to investors who can help place them in the market." Using Darwin’s digital twin technology, VCs can also optimize what opportunities they see based on their investment preferences and only see deals that are pre-qualified for them to invest in. This allows for faster and more confident investment decisions. Lastly, Darwin offers several upgrades to the VC tech stack to help VCs better manage their data internally, better manage their relationship with startups and their industry reputation in general. Q8 What’s your take on impact investing - is this a trend Darwin subscribes to when making decisions about who to partner with? The early stage investment space is such a foundational driver in shaping the future of our world. Companies who were born less than a decade ago are transforming transportation, housing, work, energy and so much more. Darwin is trying to organize the vast number of incredible innovation opportunities that exist, help shape them through the platform and present them to investors who can help place them in the market. In that spirit, we invite founders, investors, accelerators and other key stakeholders from the impact investing community to partner with Darwin in making capital allocation in impactful innovations more efficient. Not only investors and founders can join Darwin. Accelerators, experts and startup scouts are all encouraged to apply on their site . Learn more at askdarwin.com ; and check out more of the Hacker Noon Shareholder Series .

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

Startups of The Year 2024 Winners: Asia

الفوركس الفوركس 2024 الفائزين: آسيا

২০২৪ সালের বিজয়ী উদ্যোক্তা: এশিয়া

Startups des Jahres 2024 Gewinner: Asien

Startups del Año 2024 Ganadores: Asia

Startups nan ane a 2024 genyen: Azi

2024年のスタートアップ 優勝者:アジア

2024-ийн Startups Шагнагч: Ази

Startup Tahun 2024 Pemenang: Asia

Startups do Ano 2024 Vencedores: Ásia

Startups of The Year 2024 Waqtiga: Asia

Стартапи року 2024 Переможці: Азія

Startups of the Year 2024 laureatlar: Asia

Authors:
(1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg;
(2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Table of Links Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 3. MENSA: Movie Scene Saliency Dataset We define the saliency of a movie scene based on the mention of the scene in a user-written summary of the movie. If the scene appears in the summary, then it is considered salient for understanding the narrative of the movie. By aligning summary sentences to movie scenes, we identify salient scenes and later use them for movie summarization. The MENSA dataset consists of the scripts of 100 movies and respective Wikipedia plot summaries annotated with gold-standard sentence-to-scene alignment. We selected 80 movies randomly from ScriptBase (Gorinski and Lapata, 2015) and added 20 recently released, manually corrected movie scripts, which all had Wikipedia summaries. Both MENSA and ScriptBase datasets are movie scripts datasets and differ from other dialogue/narrative datasets such as SummScreenFD (Chen et al., 2022), the ForeverDreaming subset of the SummScreen dataset as used in the SCROLLS benchmark (Shaham et al., 2022). SummScreenFD is dataset of TV show episodes and consists of crowd-sourced transcripts and recaps. In contrast, the movie scripts in our dataset were written by screenwriters and the summaries were curated by Wikipedia. It is important to note that movies and TV shows have different storytelling structures, number of acts, and length. SummScreenFD has shorter input texts and summaries compared to movie scripts as shown in Table 2. 3.1 Annotation Scheme Formally, let M denote a movie script consisting of a sequence of scenes M = {S1, S2, ..., SN } and let D denote the Wikipedia plot summary consisting of a sequence of sentences D = {s1, s2, ..., sT }. The aim is to annotate and select a subset of salient scenes M′ such that M′ ⊂ M and |M′ | ≪ |M|, where for every scene in M′ there exist one or more aligned sentences in D. To manually align the summary sentences for 100 movies, we recruited five in-house annotators. They received detailed annotation instructions and were trained by the authors until they were able to perform the alignment task reliably. To analyze inter-annotator agreement, 15 movies were selected randomly and triple-annotated by the annotators. The remaining 85 movies were single annotated, similar to the annotation process used by Papalampidi et al. (2019), to reduce the cost of annotation. As annotating and aligning a full-length movie script with its summary is a difficult task, we provided a default alignment to annotators generated by the alignment model of Mirza et al. (2021). For every summary sentence, annotators first verified the default alignment with movie script scenes. If the alignment was only partially correct or missing, they corrected the alignment by adding or removing scenes for a given sentence using a web-based tool. We assume that each sentence can be aligned to one or more scenes and vice versa. In Table 1, we present statistics of the scripts and summaries in the MENSA dataset. To evaluate the quality of the annotations collected, we computed inter-annotator agreement on the triple annotated movies using three metrics: (a) Exact Match Agreement (EMA), (b) Partial Agreement (P A), and (c) Mean Annotation Distance (D). These measures were used for a similar annotation task by Papalampidi et al. (2019).[2] EMA is the ratio of the intersection of the scenes that the three annotators exactly agree upon for a given summary sentence, which is averaged over all sentences in the summary (Jaccard Similarity) and computed as follows: Partial agreement (P A) is the ratio where there is an overlap of at least one scene among the annotators and is given as follows: Annotation distance (d) for a summary sentence s between two annotators is defined as the minimum overlap distance and is computed as follows: EMA and PA between our annotators was 52.80% and 81.63%, respectively. The PA indicates that for every sentence in the summaries, there is a high overlap of at least one scene. This is consistent with the low mean annotation distance of 1.21, which indicates that on average the distance between the annotations is around one scene. The EMA shows that for more than half of the sentences, there is an exact match in scene-to-sentence alignment among the annotators. 3.2 Evaluation of Automatic Alignment Methods Since it is too expensive and time-consuming to collect gold-standard scene saliency labels for the whole of Scriptbase (Gorinski and Lapata, 2015), we generate silver-standard labels to train a model for scene saliency classification. Based on our definition of scene saliency above, silver-standard labels for scene saliency can be generated by aligning movie scenes with summary sentences. Alignment between the source document segments and the summary sentences has been previously proposed for news summarization (Chen and Bansal, 2018; Zhang et al., 2022) and narrative text (Mirza et al., 2021). Using our gold-standard labels, we investigate which of these approaches yields better alignment between movie scripts and summaries and therefore should be used to generate silver-standard labels for scene saliency. Chen and Bansal (2018) used ROUGE-L to align a summary sentence to the most similar source document sentence. In our case, we transformed these source document (movie script) sentence-level alignments to scene-level alignments such that if the scene contains the aligned sentence, the scene will be aligned to the summary sentence. Zhang et al. (2022) used a greedy algorithm for aligning the document segment and the summary sentences. For each segment, the sentences are aligned based on the gain in ROUGE-1 score. In our case, movie scenes are considered as source document segments. Mirza et al. (2021) proposed an alignment method specifically for movie scripts using semantic similarity combined with Integer Linear Programming (ILP) to align movie script scenes to summary sentences. We present the results of applying these three approaches on our gold-standard MENSA dataset in Table 3. We report macro-averaged precision (P), recall (R), and F1 score. The Mirza et al. (2021) method performs significantly better than the ROUGE-based methods, possibly as it was specifically proposed to align movie scenes and summary sentences.[3] We therefore used this alignment method to generate silver-standard scene saliency labels for the complete Scriptbase corpus. Our dataset can be used in the future to evaluate content selection strategies in long documents. The gold-standard salient scenes can also be used to evaluate extractive summarization methods. We now introduce our Select and Summarize (SELECT & SUMM) model, which first uses a classification model (Section 4) to predict the salient scenes and then utilizes only the salient scenes to generate a movie summary using a pre-trained abstractive summarization model (Section 5). These models are trained in a two-stage pipeline. This paper is available on arxiv under CC BY 4.0 DEED license. [2] We renamed total agreement in Papalampidi et al. (2019) to EMA for clarity. [3] It was also used to generate the default alignment that our human annotators had to correct, which biases our evaluation towards the method of Mirza et al. (2021). However, our results are still a good measure of how many errors human annotators find in the alignment generated by this method. Authors: (1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg; (2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Authors: Authors: (1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg; (2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Table of Links Part 1 Part 1 Part 2 Part 2 Part 3 Part 3 Part 4 Part 4 Part 5 Part 5 Part 6 Part 6 3. MENSA: Movie Scene Saliency Dataset We define the saliency of a movie scene based on the mention of the scene in a user-written summary of the movie. If the scene appears in the summary, then it is considered salient for understanding the narrative of the movie. By aligning summary sentences to movie scenes, we identify salient scenes and later use them for movie summarization. The MENSA dataset consists of the scripts of 100 movies and respective Wikipedia plot summaries annotated with gold-standard sentence-to-scene alignment. We selected 80 movies randomly from ScriptBase (Gorinski and Lapata, 2015) and added 20 recently released, manually corrected movie scripts, which all had Wikipedia summaries. Both MENSA and ScriptBase datasets are movie scripts datasets and differ from other dialogue/narrative datasets such as SummScreenFD (Chen et al., 2022), the ForeverDreaming subset of the SummScreen dataset as used in the SCROLLS benchmark (Shaham et al., 2022). SummScreenFD is dataset of TV show episodes and consists of crowd-sourced transcripts and recaps. In contrast, the movie scripts in our dataset were written by screenwriters and the summaries were curated by Wikipedia. It is important to note that movies and TV shows have different storytelling structures, number of acts, and length. SummScreenFD has shorter input texts and summaries compared to movie scripts as shown in Table 2. 3.1 Annotation Scheme Formally, let M denote a movie script consisting of a sequence of scenes M = {S1, S2, ..., SN } and let D denote the Wikipedia plot summary consisting of a sequence of sentences D = {s1, s2, ..., sT }. The aim is to annotate and select a subset of salient scenes M′ such that M′ ⊂ M and |M′ | ≪ |M|, where for every scene in M′ there exist one or more aligned sentences in D. To manually align the summary sentences for 100 movies, we recruited five in-house annotators. They received detailed annotation instructions and were trained by the authors until they were able to perform the alignment task reliably. To analyze inter-annotator agreement, 15 movies were selected randomly and triple-annotated by the annotators. The remaining 85 movies were single annotated, similar to the annotation process used by Papalampidi et al. (2019), to reduce the cost of annotation. As annotating and aligning a full-length movie script with its summary is a difficult task, we provided a default alignment to annotators generated by the alignment model of Mirza et al. (2021). For every summary sentence, annotators first verified the default alignment with movie script scenes. If the alignment was only partially correct or missing, they corrected the alignment by adding or removing scenes for a given sentence using a web-based tool. We assume that each sentence can be aligned to one or more scenes and vice versa. In Table 1, we present statistics of the scripts and summaries in the MENSA dataset. To evaluate the quality of the annotations collected, we computed inter-annotator agreement on the triple annotated movies using three metrics: (a) Exact Match Agreement (EMA), (b) Partial Agreement (P A), and (c) Mean Annotation Distance (D). These measures were used for a similar annotation task by Papalampidi et al. (2019).[2] EMA is the ratio of the intersection of the scenes that the three annotators exactly agree upon for a given summary sentence, which is averaged over all sentences in the summary (Jaccard Similarity) and computed as follows: Partial agreement (P A) is the ratio where there is an overlap of at least one scene among the annotators and is given as follows: Annotation distance (d) for a summary sentence s between two annotators is defined as the minimum overlap distance and is computed as follows: EMA and PA between our annotators was 52.80% and 81.63%, respectively. The PA indicates that for every sentence in the summaries, there is a high overlap of at least one scene. This is consistent with the low mean annotation distance of 1.21, which indicates that on average the distance between the annotations is around one scene. The EMA shows that for more than half of the sentences, there is an exact match in scene-to-sentence alignment among the annotators. 3.2 Evaluation of Automatic Alignment Methods Since it is too expensive and time-consuming to collect gold-standard scene saliency labels for the whole of Scriptbase (Gorinski and Lapata, 2015), we generate silver-standard labels to train a model for scene saliency classification. Based on our definition of scene saliency above, silver-standard labels for scene saliency can be generated by aligning movie scenes with summary sentences. Alignment between the source document segments and the summary sentences has been previously proposed for news summarization (Chen and Bansal, 2018; Zhang et al., 2022) and narrative text (Mirza et al., 2021). Using our gold-standard labels, we investigate which of these approaches yields better alignment between movie scripts and summaries and therefore should be used to generate silver-standard labels for scene saliency. Chen and Bansal (2018) used ROUGE-L to align a summary sentence to the most similar source document sentence. In our case, we transformed these source document (movie script) sentence-level alignments to scene-level alignments such that if the scene contains the aligned sentence, the scene will be aligned to the summary sentence. Zhang et al. (2022) used a greedy algorithm for aligning the document segment and the summary sentences. For each segment, the sentences are aligned based on the gain in ROUGE-1 score. In our case, movie scenes are considered as source document segments. Mirza et al. (2021) proposed an alignment method specifically for movie scripts using semantic similarity combined with Integer Linear Programming (ILP) to align movie script scenes to summary sentences. We present the results of applying these three approaches on our gold-standard MENSA dataset in Table 3. We report macro-averaged precision (P), recall (R), and F1 score. The Mirza et al. (2021) method performs significantly better than the ROUGE-based methods, possibly as it was specifically proposed to align movie scenes and summary sentences.[3] We therefore used this alignment method to generate silver-standard scene saliency labels for the complete Scriptbase corpus. Our dataset can be used in the future to evaluate content selection strategies in long documents. The gold-standard salient scenes can also be used to evaluate extractive summarization methods. We now introduce our Select and Summarize (SELECT & SUMM) model, which first uses a classification model (Section 4) to predict the salient scenes and then utilizes only the salient scenes to generate a movie summary using a pre-trained abstractive summarization model (Section 5). These models are trained in a two-stage pipeline. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [2] We renamed total agreement in Papalampidi et al. (2019) to EMA for clarity. [3] It was also used to generate the default alignment that our human annotators had to correct, which biases our evaluation towards the method of Mirza et al. (2021). However, our results are still a good measure of how many errors human annotators find in the alignment generated by this method.

This Model Knows Which Movie Scenes Matter Most

Sentient has carved out a unique path, blending blockchain technology with open-source AI to create a community-owned ecosystem. With over 1 million waitlist sign-ups for Sentient Chat in just 24 hours and a record-breaking 650,000 NFT mint for their decentralized AI model, Dobby, Sentient is redefining the future of AI. Sentient At the helm of this revolution is Himanshu Tyagi, Co-founder of Sentient and a Professor at the Indian Institute of Science, whose expertise in AI and blockchain is driving innovations like the Reasoning Agent and a Perplexity-rivaling chatbot. We sit down with Himanshu to explore the technical challenges, groundbreaking solutions, and ambitious vision behind Sentient’s rise. Ishan Pandey: Sentient Chat has exploded onto the scene with over 1 million waitlist sign-ups in under 24 hours. What technical challenges did you face in building an AI platform that’s both decentralized and consumer-friendly? Ishan Pandey: Himanshu Tyagi: Decentralization is foundational to our mission, extending far beyond just Sentient Chat. We believe that the future of AI must be open and community-driven, as decentralization uniquely empowers active community participation. This philosophy cascades directly into our products—most notably, Sentient Chat. Himanshu Tyagi: With Sentient Chat, our goal is to ensure that the community truly feels ownership. Users aren't merely passive consumers; they are creators, molders, and stewards of the platform. They have full control—from the underlying AI model and agents, to data and governance—enabling them to shape the product precisely to fit their diverse needs and use cases. Critically, decentralization and usability reinforce each other in Sentient Chat. By empowering users to customize and enhance the platform, every innovation developed by one user or team becomes accessible to the broader community. This collective contribution enriches the platform's usability, continually driving improvements and innovations that benefit everyone involved. Ultimately, decentralization doesn't complicate the user experience—it enhances it, creating a vibrant ecosystem where collective creativity and collaboration translate directly into a more powerful, flexible, and user-friendly platform. Ishan Pandey: You’ve positioned Sentient Chat as a competitor to Perplexity, powered by Dobby, the world’s first community-owned AI model. Can you walk us through the technical innovation behind Dobby and how it supports Sentient Chat’s unique capabilities? Ishan Pandey: Himanshu Tyagi: AI is rapidly becoming the intermediary between information networks and users, granting it significant power to influence user perspectives based on inherent biases. Our goal is to empower communities by enabling them to align AI models according to their specific values, thus promoting a diverse ecosystem of "Loyal AI" models. While biases will always exist due to training data, users should not be limited to generalized models that simply average internet opinions. Instead, we envision users selecting from a wide array of models aligned with distinct community values. Himanshu Tyagi: Dobby represents the first of such models, explicitly aligned with the crypto community's values—pro-crypto and pro-personal freedom. With Dobby, we've achieved three notable technical breakthroughs: Effective Value Alignment Without Performance Loss: Aligning AI models to specific values through fine-tuning is notoriously challenging, often leading to degraded performance and incoherent outputs. With Dobby, we've successfully fine-tuned the Llama model to embrace specific values while maintaining excellent performance across critical benchmarks.


Human-centric Tone and Freedom of Expression with Safety: Achieving our desired alignment required removing existing guardrails from Llama and carefully rebuilding them. Surprisingly, this process enhanced the model’s tone, resulting in more natural and human-centric interactions. Even with relaxed constraints, Dobby remains safe, as demonstrated by metrics from our "Sorry Bench." Moreover, our Dobby-mini 8B and Dobby 70B models exhibit granular control over specific safety dimensions (such as hate speech, financial, medical, and legal advice, and explicit content), allowing us precise adjustments of the model's safety profile.


Fingerprinting for the Most Distributed Model Ownership Ever: Dobby is currently the most widely distributed model, with over 660,000 owners. This unprecedented level of distribution is facilitated by our innovative fingerprinting technology. Fingerprinting injects unique key-response pairs into the model, enabling robust verification of ownership. These fingerprints have a negligible impact on overall model performance and are resilient to removal through fine-tuning, model merging, or other modifications. Effective Value Alignment Without Performance Loss: Aligning AI models to specific values through fine-tuning is notoriously challenging, often leading to degraded performance and incoherent outputs. With Dobby, we've successfully fine-tuned the Llama model to embrace specific values while maintaining excellent performance across critical benchmarks. Effective Value Alignment Without Performance Loss: Aligning AI models to specific values through fine-tuning is notoriously challenging, often leading to degraded performance and incoherent outputs. With Dobby, we've successfully fine-tuned the Llama model to embrace specific values while maintaining excellent performance across critical benchmarks. Effective Value Alignment Without Performance Loss: Human-centric Tone and Freedom of Expression with Safety: Achieving our desired alignment required removing existing guardrails from Llama and carefully rebuilding them. Surprisingly, this process enhanced the model’s tone, resulting in more natural and human-centric interactions. Even with relaxed constraints, Dobby remains safe, as demonstrated by metrics from our "Sorry Bench." Moreover, our Dobby-mini 8B and Dobby 70B models exhibit granular control over specific safety dimensions (such as hate speech, financial, medical, and legal advice, and explicit content), allowing us precise adjustments of the model's safety profile. Human-centric Tone and Freedom of Expression with Safety: Achieving our desired alignment required removing existing guardrails from Llama and carefully rebuilding them. Surprisingly, this process enhanced the model’s tone, resulting in more natural and human-centric interactions. Even with relaxed constraints, Dobby remains safe, as demonstrated by metrics from our "Sorry Bench." Moreover, our Dobby-mini 8B and Dobby 70B models exhibit granular control over specific safety dimensions (such as hate speech, financial, medical, and legal advice, and explicit content), allowing us precise adjustments of the model's safety profile. Human-centric Tone and Freedom of Expression with Safety: Fingerprinting for the Most Distributed Model Ownership Ever: Dobby is currently the most widely distributed model, with over 660,000 owners. This unprecedented level of distribution is facilitated by our innovative fingerprinting technology. Fingerprinting injects unique key-response pairs into the model, enabling robust verification of ownership. These fingerprints have a negligible impact on overall model performance and are resilient to removal through fine-tuning, model merging, or other modifications. Fingerprinting for the Most Distributed Model Ownership Ever: Dobby is currently the most widely distributed model, with over 660,000 owners. This unprecedented level of distribution is facilitated by our innovative fingerprinting technology. Fingerprinting injects unique key-response pairs into the model, enabling robust verification of ownership. These fingerprints have a negligible impact on overall model performance and are resilient to removal through fine-tuning, model merging, or other modifications. Fingerprinting for the Most Distributed Model Ownership Ever: Sentient Chat is our AI-powered search platform, designed to showcase and provide users with a direct experience of the intangible concept of Loyal AI. This platform demonstrates the competitive edge of open-source ecosystems, successfully outperforming platforms like Perplexity and matching the search benchmarks of ChatGPT. Powered by Dobby, Sentient Chat illustrates how Loyal AI can directly influence the information delivered to users, presenting content concisely and engagingly. The integration of Dobby into Sentient Chat has notably improved the efficiency and entertainment value of search interactions. With Dobby's clear, human-like, no-nonsense communication style, users can quickly and effectively access the information they seek, highlighting the platform's distinct advantage in AI-driven search experiences. Ishan Pandey: One of the most intriguing aspects of Sentient Chat is its Reasoning Agent, equipped with tools like search, calculator, and think. How did you architect this agent, and what technical hurdles did you overcome to make reasoning a core strength? Ishan Pandey: Himanshu Tyagi: Recent advancements have shown that generating executable Python code for tool calling yields a significant boost in performance compared to conventional JSON-based approaches. In particular, LLM’s are inherently adept at compressing the action-space of tasks using code. This naturally prompted the use of code as the mode of expression for our reasoning agent. Sometimes LLM's write code that does not execute/run the way it is intended to, this is remedied by a Python interpreter which checks if the code generated executes or throws errors, if it throws errors, then it will keep regenerating the code (with the previous error traces) until it outputs satisfactory code. Himanshu Tyagi: Another caveat is that the LLM may generate functional code that does not do what it is intended to do (e.g. calls the calculator tool instead of searching); the reasoning agent is capable of digesting the output of the information and assessing whether it fits with, on a general level, what it expects from the particular code/tool call. We tested two different types of agents in the Open Reasoning Agent portion of the ODS framework: Chain-of-Thought with ReAct agent and Chain-of-Code with CodeAct agent. We tested two different types of agents in the Open Reasoning Agent portion of the ODS framework: Chain-of-Thought with ReAct agent and Chain-of-Code with CodeAct agent. In the Chain-of-Thought ReAct architecture, the agent follows an iterative reasoning-action loop. First, the 'Think' step allows the agent to reason internally, articulating plans or interpreting intermediate results in natural language. Whenever the agent encounters uncertainty or lacks critical information, it invokes the 'Search' tool, actively retrieving external information from the web. For arithmetic or computational tasks that require high precision, the agent utilizes the 'Calculate' tool. Thus, CoT-ReAct continuously cycles through thinking, searching, and calculating as needed, until it arrives at a reliable solution. On the other hand, the Chain-of-Code or CodeAct agent employs executable code to perform its reasoning tasks. Initially, the agent thinks through its strategy, similar to CoT-ReAct. However, instead of relying purely on natural language, it generates executable Python code as part of its reasoning. The 'Calculate' function is seamlessly embedded here, directly integrated into the Python execution environment. This allows for accurate computations and algorithmic reasoning. Like CoT-ReAct, CodeAct also uses the 'Search' tool when additional external information is necessary, incorporating the results directly into its code-generation and execution processes. The key difference lies in how each agent interacts with these tools. For CoT-ReAct, 'Think', 'Search', and 'Calculate' are distinctly separate actions explicitly chosen during reasoning. For CodeAct, while 'Think' remains an explicit planning step, 'Calculate' is inherently built into the code execution itself, making computational tasks integral rather than external. 'Search' remains similarly explicit in both approaches, providing vital external context whenever the agent's internal knowledge is insufficient. Ishan Pandey: Sentient completed a record-breaking 650,000 NFT mint for Dobby, tying ownership to a decentralized AI model. What technical infrastructure did you build to handle this scale, and how does blockchain enhance the community ownership aspect? Ishan Pandey: Himanshu Tyagi: This was not a technically challenging problem – modern blockchains can easily handle such load. The most interesting part here is that this is the first time anybody has demonstrated this scale of “direct democracy” in model governance. In centralized companies, this governance is done by a tiny alignment team which has sometimes even modified the model to match its own beliefs. Anthropic had done some simple experiments under the name of constitutional AI with about 1000 people, but nothing close to this open governance by community. We wanted to give this power to the people and blockchains are perfect instruments for that. Dobby community already decided what kind of persona Dobby should have, leashed or unhinged (they chose unhinged over leashed). In future, all decisions about Dobby-unhinged model will be taken by this community, who will also get rewarded as model usage increases. Himanshu Tyagi: Going forward, these ownership NFTs will be converted to ownership tokens on our blockchain which can be used to govern alignment and updates to this model. Furthermore, the associated fingerprints will be committed as Merkle root to the same contract – anyone can verify the identity of the model by asking our fingerprint queries, and can check that the same queries are committed with the ownership contract. Ishan Pandey: Your Reasoning Agent integrates real-time tools and aims to tackle ambiguous, subjective tasks. How does this differ from existing reasoning agents, like OpenAI’s Deep Research, and what novel solutions did you implement to push the boundaries of AI reasoning? Ishan Pandey: Himanshu Tyagi: When comparing our open-source platform to closed-source competitors, the primary competitive advantage of closed systems has traditionally been their technology. However, platforms like Sentient Chat and Open Data Sources (ODS) have effectively leveled the playing field by democratizing AI knowledge, making our framework completely open to the community. Closed-source platforms, such as those by OpenAI, operate as black boxes; neither you, I, nor anyone else can fully comprehend their internal processes. Himanshu Tyagi: In contrast, our open framework invites continuous innovation from the wider community, enabling rapid identification and resolution of gaps that might otherwise remain unnoticed. This collaborative, open-input and open-output approach is key to building the best possible search platform. Our ultimate vision includes enabling users to contribute their own data sources and agents, thereby drastically enhancing Sentient Chat and ODS through collective, community-driven improvements. Ishan Pandey: You’ve emphasized Sentient Chat’s edge over competitors like Perplexity with its 15+ AI agent integration. What technical challenges did integrating multiple agents into a single chatbot interface present, and how did you solve them? Ishan Pandey: Himanshu Tyagi: Technically, we are addressing many complex software and design challenges to allow a broad variety of agents to participate in a singular user experience through Sentient Chat. We are also offering many tools to these agent builders such as our state-of-the-art AI search APIs, the Sentient Secure Enclave offering for building “unruggable agents,” etc. But more than addressing these technical challenges, the most interesting part here is the possibility of a completely new way to access internet knowledge. Himanshu Tyagi: For decades, Google has dominated search by focusing primarily on helping users find information online. Google's reliance on advertising revenue, rooted in recommending sources for information, inherently limits its ability to innovate beyond traditional information retrieval. AI, on the other hand, provides an opportunity to bypass mere information gathering, enabling users to directly execute tasks without needing separate analysis and action phases, which is our exact competitive advantage against the behemoth of Google. This ambitious vision requires a diverse ecosystem—one that integrates varied indexed data sources with numerous AI agents capable of performing specialized actions. The key to unlocking this potential lies in creating a transparent and open platform, actively incentivizing broad community participation. Data providers must clearly understand the value their contributions bring, while agent developers must seamlessly integrate their services to enhance overall functionality. Such an ecosystem must be community-governed, ensuring fairness, openness, and continual innovation driven by collective contribution and collaboration. By the way, a lot more than 15 agents are coming on Sentient Chat! Ishan Pandey: Looking ahead, what’s next for Sentient in terms of technical innovation? How do you plan to evolve the Reasoning Agent and expand the open AGI ecosystem? Ishan Pandey: Himanshu Tyagi: We see a future where AI becomes an intermediary between all human senses, global information, and social networks. Sentient’s vision is to make sure that this AI is loyal to us. This singular objective drives all our development. For reasoning agents, we want to make sure that developers get access to models whose reasoning capabilities and skills are aligned with their interests or the use case. Himanshu Tyagi: For instance, if you are building a sovereign agent that can control wallets, the underlying models should have reasoning capabilities to ensure that malicious actors cannot manipulate prompts and steal funds. This is alignment – we will be building such aligned models. Every developer building in the Open AGI ecosystem should have the option of using AI models that will be loyal to their application or users. Sentient Chat is the collective offering of all such agents built on Loyal AI. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: business blogging program business blogging program

How Sentient’s Reasoning Agent is Outsmarting the Competition: Inside Look 

كيف يعمل عميل التفكير في Sentient على تجنب المنافسة: النظر في الداخل

Com l'agent de raonament de Sentient està superant la competència: a l'interior

Cómo el agente de razonamiento de Sentient está superando a la competencia: Inside Look

Bagaimana Agen Penalaran Sentient Mengalahkan Pesaingnya: Pandangan dari Dalam

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Sentient の推論エージェントが競合他社を凌駕する方法: 内部の分析

Како резонантниот агент на Sentient ја надмудри конкуренцијата: погледнете одвнатре 

Bagaimana Sentient's Reasoning Agent Mengabaikan Persaingan: Melihat Dalam

Imaynatachus Sentient’s Reasoning Agent atipanakuymanta aswan yachayniyuq kachkan: Ukhupi qhaway 

Как рассуждающий агент Sentient вытесняет конкуренцию: Внутренний взгляд

Sida Wakiilka Sababta Sentient uu uga Wanagsan yahay Tartanka: Gudaha Fiiri 

Sentient的推理代理如何超越竞争:内部看法

Since graduating Bitcoin's class of 2022, I've been making Bitcoin and Crypto related posts on X.com (formerly Twitter). As we start this year, I thought I'd share some of my best ones. Cheers. 1. Bitcoin is the U235 of today's global socio-economy. Stacking and Hodling = Refining this U235. One day, it shall economic boom! 2. Shitcoining is bad but it's launch-fast-fail-fast model seems to match the rocketry model of SpaceX. 3. Bitcoin LN projects should break out into the hardware space. Hodling - The act of holding onto Bitcoin for the long term. Hodl Building - Hodling BTC while supporting Building initiatives for hardware, software, and community. I support Hodl Building. 5. Bitcoin will stop the next asteroid impact. 6. What if MSTR and NVDA merge? 7. What if a house is literally built on top of a seed phrase containing 1 BTC, how valuable is that house?! 8. The Lord sent us BTC to change the hearts of Central Bankers 🙏 9. Just imagined the Isaac Newton, Master of the Mint, wasting his talents determing the purity of gold coins. Trivial wok settled 1900 years prior by Archimedes. If only he had lived in the era of your BTC standard, he would've met an econometrics challenge worthy of his mind 😎 10. In this article, the author proposes a BTC stablecoin running on Rootstock, backed by NUCLEAR WARHEADS!!🤯 But negatively backed, no worries😅. 11. "State money deserves critique, and Bitcoiners rarely miss an opportunity. Yet it deserves accurate critique; anything less is counterproductive" -- Eric Voskuil 12. Bitcoin will keep the D.O.G.E efficient. 13. Vote Bitcoin. 14. Altcoins are not energy efficient. Altcoins are energy poor. 15. The Fed is a dollar-producing machine. Bitcoin is a dollar-eating machine. 16. Bitcoin will align AI. 17. Bitcoin will tame the AGI beast. 18. Random idea: A cryptocurrency called AA- AstroAgric. Created by farmers and astronauts who both write code, they build rockets to explore outerdspace, powered by corn methanol. Mining of the coin is fueled by burning biomass from gardens. Climate change is no issue... 19. Prayer of the Altcoin CEO: "We ask you Lord that Bitcoin continues to let us eat the crumbs that fall from its table". 20. What If: Early on, all governments started mining #Bitcoin as global reserve currency instead of trying to sabotage it 🤓 Where would we be now?! 21. Damn it. I have more reason to disagree with Maj. Jason Lowery's points 😒 When comparing Bitcoin to Gunpowder: Guns are centralized power projectors (all the power is packed in the bullet). Bitcoin is a decentralized power projector. My Quantum Era 22. Hard to imagine a Black Hat quantum-computer-hacker. Coz these computers are a really publicity affair (like warehouse-sized supercomputers). So I suffice future quantum-computer-hackers will only (ok, mostly) be White Hats. Who knows game theory, let's we play this game. 23. Thinking of building a quantum bitcoin clock. The most useful precision instrument for telling time ever devised. 24. I think a quantum computer could be used to do cheaper #Bitcoin coinjoins. 25. With @pleblira 's help, I've modelled some BTC blockchain data on a quantum computer. 😎📷 github.com/MarvinMK-bit/B……... 26. Somebody share their not-too-important BitVM NAND trees with me so I can model them on a quantum computer please 🙏 Externalised classical processing is now old news 😁 27. I have successfully modelled the Bitcoin Supply Equation with 5 Qubits using the devices at IBM Quantum! 😎✨ 28. Bitcoin is the most quantum-mechanical electrical energy system in the world. Back to Bitcoin 29. Bitcoin is building community. 30. Bitcoin is The AGI. It is artificial. It is generally for all. It is intelligent as it has outsmarted every investor, trader, speculator combined. 31. More Bitcoin. 32. Wow 😮 From a World Wide 🕷 Web to a World Wide Hornets 🐝 nest. Let's goooo. #Bitcoin 33. Bitcoin loves D.O.G.E 34. Has Hinton heard about Bitcoin. No corporate entity owns its means of computation. 35. Bitcoin is really not good good for criminal activities. BTC is for love, peace and productivity. Plus being a pest to whoever doesn't get it 😄 Vote Bitcoin. Oh Yeah. Bitcoin keys should be the car ignition keys 🤗 37. 😐 why don't you people just explore Bitcoin? 38. How can a Bitcoin monk have a pot belly? 39. 40. Bitcoin will one day be backed by the most powerful military force in the world. Their job will be to protect the network against terrorist attacks. End Note 1 - The Bitcoin Protectors. I ended my last tweet by saying; Bitcoin will one day be backed by the most powerful military force in the world. Their job will be to protect the network against terrorist attacks. Today, it doesn't happen. All anybody can think of is how it could be funding terrorist attacks, and the fast track to being a doomed player of SQUID game. But since Bitcoin is more efficient at transferring economic energy through time (into the future) and space (to another person), BTC will hone the skills of protectors better, given it forces them to hodl and lower their time preferences hence short-sighted consumerist mannerisms. Bitcoin will create the most honest, long suffering, good willed protector group we ever saw. No matter their hiring agency. People willing to keep delaying gratification. For the greater good. (I was wrong. In my previous article. Bitcoin economic energy might take 50 years to charge up to saturation, but spending it will take long as well. Because bitcoiners don't waste energy, and quick spending with little planning wastes energy.) A lot of movies today show how intelligence work is full of perpetual lies and backstabbing. Just watched the highly rated 'The Day of The Jackal'. It had lots of senseless killing. And RIVER doesn't make sense. Even Bitcoiners don't reveal where their funds are going exactly. But see, they don't have to. All we have to see is who is starving and who is getting way more than they deserve, and we can RIVER those flows with our imaginations. But the question is the hired guns. How could they play at their tense games better? The writer and author, David Brin, once gave a talk at Google in which he spoke like a Bitcoiner. He still isn't. Here's the parts that caught my attention (with text in brackets added my me) -- With sousveillance, we remind the professional protector caste (FBI, NSA, D.O.G.E…), the surveillance watchdogs, that they are a dog (and not a god) - David Brin Then on the 'Age of Amateurs' -- “ The Age of Amateurs (where citizens themselves take on previously organizationally centralized roles e.g. journalism) is not so as to get rid of the professional protectors, but in order to augment them because we are going to need healthy institutions, we’re going to need healthy accountability arenas … and we’re going to need the advanced reliency that comes from a fully empowered populous. What empowers like Bitcoin, tell me. I do not see it. "where is the ritualized combat that makes bad stuff go away", he asks. Bitcoin is that ritualized combat. As bloodless as possible, yet, it cuts through a lot of noise and aids in "maximizing the test of product (from our citizen everything-doers) while minimizing the cost". *** End Note 2 - The Bitcoin Computer Hobbyists The personal computer revolution was started by hobbyists in the Homebrew Computer Club that included Steve Wozniak, co-founder of Apple (and sole engineer of the first two Apple PCs). Here is an interesting parallel. While they believed in free sharing of information, pranks, and tweaking computer parts as needed, Bitcoiners believe in free sharing of sats, calling out sh*tcoiners (our version of pranks), and tweaking Bitcoin as we been doing with lots of arguments and hacks over block size, protocol up-dates, future usecases. So it isn't perfect. No PC from the Homebrew Computer Club could have been called perfect. Indeed, as Bitcoin gets more mainstream, some of the fire of the early people will wane. It already is waning, courtesy of new memecoins like $TRUMP. If US Bitcoiners get their Strategic Reserve, it will be quite the miracle. Otherwise, with all this crypto fanfare, I do not see it flying. Well, we are still early. 1 day to go. Cheers once again. Since graduating Bitcoin's class of 2022, I've been making Bitcoin and Crypto related posts on X.com (formerly Twitter). As we start this year, I thought I'd share some of my best ones. Cheers. 1. Bitcoin is the U235 of today's global socio-economy. Stacking and Hodling = Refining this U235. One day, it shall economic boom! 2. Shitcoining is bad but it's launch-fast-fail-fast model seems to match the rocketry model of SpaceX. 3. Bitcoin LN projects should break out into the hardware space. Hodling - The act of holding onto Bitcoin for the long term. Hodling - The act of holding onto Bitcoin for the long term. Hodl Building - Hodling BTC while supporting Building initiatives for hardware, software, and community. I support Hodl Building. 5. Bitcoin will stop the next asteroid impact. 6. What if MSTR and NVDA merge? 7. What if a house is literally built on top of a seed phrase containing 1 BTC, how valuable is that house?! 8. The Lord sent us BTC to change the hearts of Central Bankers 🙏 9. Just imagined the Isaac Newton, Master of the Mint, wasting his talents determing the purity of gold coins. Trivial wok settled 1900 years prior by Archimedes. If only he had lived in the era of your BTC standard, he would've met an econometrics challenge worthy of his mind 😎 10. In this article, the author proposes a BTC stablecoin running on Rootstock, backed by NUCLEAR WARHEADS!!🤯 But negatively backed, no worries😅. 11. "State money deserves critique, and Bitcoiners rarely miss an opportunity. Yet it deserves accurate critique; anything less is counterproductive" -- Eric Voskuil 12. Bitcoin will keep the D.O.G.E efficient. 13. Vote Bitcoin. 14. Altcoins are not energy efficient. Altcoins are energy poor. 15. The Fed is a dollar-producing machine. Bitcoin is a dollar-eating machine. 15. The Fed is a dollar-producing machine. Bitcoin is a dollar-eating machine. 16. Bitcoin will align AI. 17. Bitcoin will tame the AGI beast. 18. Random idea: A cryptocurrency called AA- AstroAgric. Created by farmers and astronauts who both write code, they build rockets to explore outerdspace, powered by corn methanol. Mining of the coin is fueled by burning biomass from gardens. Climate change is no issue... 19. Prayer of the Altcoin CEO: "We ask you Lord that Bitcoin continues to let us eat the crumbs that fall from its table". 20. What If: Early on, all governments started mining #Bitcoin as global reserve currency instead of trying to sabotage it 🤓 Where would we be now?! 21. Damn it. I have more reason to disagree with Maj. Jason Lowery's points 😒 When comparing Bitcoin to Gunpowder: Guns are centralized power projectors (all the power is packed in the bullet). Bitcoin is a decentralized power projector. My Quantum Era 22. Hard to imagine a Black Hat quantum-computer-hacker. Coz these computers are a really publicity affair (like warehouse-sized supercomputers). So I suffice future quantum-computer-hackers will only (ok, mostly) be White Hats. Who knows game theory, let's we play this game. 23. Thinking of building a quantum bitcoin clock. The most useful precision instrument for telling time ever devised. 24. I think a quantum computer could be used to do cheaper #Bitcoin coinjoins. 25. With @pleblira 's help, I've modelled some BTC blockchain data on a quantum computer. 😎📷 github.com/MarvinMK-bit/B……... 26. Somebody share their not-too-important BitVM NAND trees with me so I can model them on a quantum computer please 🙏 Externalised classical processing is now old news 😁 27. I have successfully modelled the Bitcoin Supply Equation with 5 Qubits using the devices at IBM Quantum! 😎✨ 28. Bitcoin is the most quantum-mechanical electrical energy system in the world. Back to Bitcoin 29. Bitcoin is building community. 30. Bitcoin is The AGI. It is artificial. It is generally for all. It is intelligent as it has outsmarted every investor, trader, speculator combined. 31. More Bitcoin. 32. Wow 😮 From a World Wide 🕷 Web to a World Wide Hornets 🐝 nest. Let's goooo. #Bitcoin 33. Bitcoin loves D.O.G.E 34. Has Hinton heard about Bitcoin. No corporate entity owns its means of computation. 35. Bitcoin is really not good good for criminal activities. BTC is for love, peace and productivity. Plus being a pest to whoever doesn't get it 😄 Vote Bitcoin. Oh Yeah. Bitcoin keys should be the car ignition keys 🤗 37. 😐 why don't you people just explore Bitcoin? 38. How can a Bitcoin monk have a pot belly? 39. 40. Bitcoin will one day be backed by the most powerful military force in the world. Their job will be to protect the network against terrorist attacks. End Note 1 - The Bitcoin Protectors. I ended my last tweet by saying; Bitcoin will one day be backed by the most powerful military force in the world. Bitcoin will one day be backed by the most powerful military force in the world. Their job will be to protect the network against terrorist attacks. Their job will be to protect the network against terrorist attacks. Today, it doesn't happen. All anybody can think of is how it could be funding terrorist attacks, and the fast track to being a doomed player of SQUID game. But since Bitcoin is more efficient at transferring economic energy through time (into the future) and space (to another person), BTC will hone the skills of protectors better, given it forces them to hodl and lower their time preferences hence short-sighted consumerist mannerisms. Bitcoin will create the most honest, long suffering, good willed protector group we ever saw. No matter their hiring agency. People willing to keep delaying gratification. For the greater good. (I was wrong. In my previous article. Bitcoin economic energy might take 50 years to charge up to saturation, but spending it will take long as well. Because bitcoiners don't waste energy, and quick spending with little planning wastes energy.) A lot of movies today show how intelligence work is full of perpetual lies and backstabbing. Just watched the highly rated 'The Day of The Jackal'. It had lots of senseless killing. And RIVER doesn't make sense. Even Bitcoiners don't reveal where their funds are going exactly. But see, they don't have to. All we have to see is who is starving and who is getting way more than they deserve, and we can RIVER those flows with our imaginations. But the question is the hired guns. How could they play at their tense games better? The writer and author, David Brin, once gave a talk at Google in which he spoke like a Bitcoiner. once gave a talk at Google He still isn't. Here's the parts that caught my attention (with text in brackets added my me) -- With sousveillance, we remind the professional protector caste (FBI, NSA, D.O.G.E…), the surveillance watchdogs, that they are a dog (and not a god) - David Brin With sousveillance, we remind the professional protector caste (FBI, NSA, D.O.G.E…), the surveillance watchdogs, that they are a dog (and not a god) - Then on the 'Age of Amateurs' -- “ The Age of Amateurs (where citizens themselves take on previously organizationally centralized roles e.g. journalism) is not so as to get rid of the professional protectors, but in order to augment them because we are going to need healthy institutions, we’re going to need healthy accountability arenas … and we’re going to need the advanced reliency that comes from a fully empowered populous. “ The Age of Amateurs (where citizens themselves take on previously organizationally centralized roles e.g. journalism) is not so as to get rid of the professional protectors, but in order to augment them because we are going to need healthy institutions, we’re going to need healthy accountability arenas … and we’re going to need the advanced reliency that comes from a fully empowered populous. What empowers like Bitcoin, tell me. I do not see it. "where is the ritualized combat that makes bad stuff go away", he asks. "where is the ritualized combat that makes bad stuff go away", Bitcoin is that ritualized combat. As bloodless as possible, yet, it cuts through a lot of noise and aids in "maximizing the test of product (from our citizen everything-doers) while minimizing the cost". *** End Note 2 - The Bitcoin Computer Hobbyists The personal computer revolution was started by hobbyists in the Homebrew Computer Club that included Steve Wozniak, co-founder of Apple (and sole engineer of the first two Apple PCs). Here is an interesting parallel. While they believed in free sharing of information, pranks, and tweaking computer parts as needed, Bitcoiners believe in free sharing of sats, calling out sh*tcoiners (our version of pranks), and tweaking Bitcoin as we been doing with lots of arguments and hacks over block size, protocol up-dates, future usecases. So it isn't perfect. No PC from the Homebrew Computer Club could have been called perfect. Indeed, as Bitcoin gets more mainstream, some of the fire of the early people will wane. It already is waning, courtesy of new memecoins like $TRUMP. If US Bitcoiners get their Strategic Reserve, it will be quite the miracle. Otherwise, with all this crypto fanfare, I do not see it flying. Well, we are still early. 1 day to go. Cheers once again.

40 BTC Sayings in 280 Characters or Less

40 مقولة عن عملة البيتكوين في 280 حرفًا أو أقل

40 BTC Aruchjanakax 280 Caracteres ukan jan ukax juk’ampi

40 BTC výroků ve 280 znacích nebo méně

40 BTC-Sprüche in 280 Zeichen oder weniger

40 Ρήσεις BTC σε 280 χαρακτήρες ή λιγότερους

40 dichos de BTC en 280 caracteres o menos

280 अक्षरों या उससे कम में 40 BTC कथन

40 Ucapan BTC dalam 280 Karakter atau Kurang

280文字以内のBTCに関する格言40選

40 BTC გამონათქვამი 280 სიმბოლოში ან ნაკლები

40 BTC ເວົ້າໃນ 280 ຕົວອັກສອນຫຼືຫນ້ອຍ

BTC කියමන් 40ක් අක්ෂර 280කින් හෝ ඊට අඩුවෙන්

Artificial intelligence is rampant in schools, but education professionals aren’t teaching students the importance of ethical utilization. The problem is that it’s a complex topic, and educators simply don’t have the time or resources to tackle it. Where do they get the relevant materials? How do they fit it into their existing lesson plans? Here’s how questions like these are holding AI ethics classes back — and what decision-makers are doing to fix the problem. The Importance of Teaching AI Ethics in School AI ethics is a set of principles that govern the development, deployment and utilization of algorithm-based tools. They are meant to guide stakeholders, minimizing the damage such technologies can do to the environment, society or the economy. On a smaller scale, they focus on preventing bias, discrimination, misinformation and abuse. Teaching AI ethics in school may seem unnecessary, but its importance cannot be overstated. AI has already become a staple in many industries. According to a McKinsey & Company report, 72% of organizations use it for at least one business function. If its upward trajectory continues, its impact could rival that of computers, smartphones or the internet. Avoiding talking about AI in the classroom because of its newness needlessly repeats history. It is the same as years ago when students were told they wouldn’t always have a calculator in their back pocket right after the smartphone was invented. This technology’s popularity is growing in both personal and professional domains. Students must be prepared to use it responsibly when entering the workforce to ensure better outcomes for their industry and society. AI is powerful. Advanced algorithms can analyze massive datasets exponentially faster than humans. They excel at pattern recognition, trend prediction and content creation. Whether educators like it or not, AI will likely change the world. If they prepare their students accordingly, they can ensure that change is positive. Ethical Dilemmas Students Face When Using AI Typical ethical risks students face when using AI include technology abuse, harassment and plagiarism. The latter is one of the most common. Thanks to generative tools, academic dishonesty is easy to get away with. Google Trends reveals that searches for “AI essay writing” increased by a staggering 2,041% after ChatGPT’s release. Unfortunately, plagiarism isn’t the most severe offense. Lately, university, high school and even middle school students have been caught using generative tools to take part in deepfake-related sex crimes. In South Korea, one journalist uncovered one such group that had over 2,000 members. Many victims were believed to be under 16 — under Korea’s age of consent. Even if kids aren’t using regular photos of their classmates to create explicit material, AI use still revolves around consent. This concept revisits data privacy and security principles — all data end users enter is no longer private. The platform or service provider may use it to train their model or enhance their marketing strategies. Many of today’s students view generative and machine learning models as entertaining, harmless tools — even when they are actively harming others. Teachers have a unique opportunity to educate them early on about the consequences of such actions, stopping unacceptable behavior like this at the source. Is Ethical AI Use Being Taught in Schools Today? School board standards, classroom policies and state laws vary dramatically, so AI adoption has been erratic at best. This split between those who embrace it and those who don’t has stalled standardization. However, some are still taking a chance on ethics courses. Elementary School While various academics and industry experts argue ethics discussions should start in elementary school, relatively few institutions even use algorithm-based tools that early. There are virtually no public resources available for grade school administrators. Middle and High School Many middle and high schools aren’t discussing the intricacies of ethical AI. Instead, they are cracking down on AI use. Studies show that the accuracy of AI detection tools is typically about 50%, so most instances slip by unnoticed. As a result, kids often learn bad habits. Promisingly, some decision-makers have seen the need for AI ethics. For instance, California Governor Gavin Newsom signed Bill No. 2876 into law in September 2024. When curriculum frameworks are revised in January 2025, schools must incorporate AI literacy content — which guides critical and ethical use — into the classroom. Not all educators receive playbooks from experts. Some, like Jeff Simon, a mathematics teacher at Sage Creek High School, are navigating this new field on their own. Simon said he is trying to “teach the ethical use” of AI since it is  “just going to keep on growing.” College and University Most AI ethics classes are offered at higher education institutions because working those discussions into data science or computer engineering courses is easier. At the very least, many have developed policies for generative AI use. Reasons Why AI Ethics Isn’t Being Taught in School AI is not just present in education — it is thriving. For reference, in 2024, 86% of students admitted to using AI for schoolwork. Despite its popularity, many schools have no guiding principles on how to use these models ethically. Time is one of the main constraints — educators face the impossible task of doing more with less. They already spend 54 hours at work per week. How will they learn about AI and develop classroom policies that align with ethics in addition to teaching curriculum, grading papers and creating lesson plans? The field of AI is complex and rapidly evolving. Even with district-wide support, educational materials may quickly become outdated. Many schools still use decades-old textbooks to teach, so any solution would require extensive retrofitting. Moreover, how do they send home ethics homework? What would tests look like? How would they assign fair grades? Educators may be willing to embrace the concept of ethical AI classes if given the resources, but there’s no clear framework on how that could be done. Should they begin teaching it in elementary school? Will it replace a class, be offered as an elective or be integrated into the existing curriculum? There’s no one-size-fits-all solution. Many teachers have adopted “not in my classroom” policies in response to these concerns. As a result, students likely won’t learn how to use these tools the right way. Many will take whatever inaccurate, unregulated knowledge they cobble together from peers, parents and the internet as the truth — which may have far-reaching impacts. Addressing Concerns to Make AI Ethics Classes a Reality There are several ways education professionals can address why AI ethics isn’t being taught in schools. Place AI Ethics Wherever It Fits Integrating AI ethics into the classroom can mean creating a new elective or squeezing it in at the end of the period. Placing it where it naturally appears is best. For example, when students are assigned an eight-page essay, they can hear about responsible generative AI use. Teachers and administrators should adapt these new courses to students’ grade, technical expertise and knowledge levels to make the subject matter age-appropriate and engaging. Assignments should be relatively open-ended but follow a clear ethical framework. Tailor Subject Matter for End Users Typically, conversations about ethical AI focus on data privacy and output bias, centering around people who develop, deploy or manage these tools. End users interact with the user interface — not the backend — so their approach must differ slightly. Core principles should be accountability, value alignment, explainability, fairness and integrity. Pupils should learn critical thinking to combat bias, reasoning to prevent overreliance and objective analysis to mitigate the spread of misinformation. Model Responsible AI Use If state law or school board policies make teaching the ethics of AI in the classroom difficult, educators should take advantage of workarounds. Modeling responsible, honorable behavior goes a long way. Alternatively, they can invite a guest speaker. Facilitate In-Class Ethics Discussions Facilitating discussions, debates and group work in class lets high school teachers and college professors act as moderators. Posing questions about data de-identification, copyright infringement and summarization as they relate to AI can initiate productive conversations. Modern Lessons for a Modern Age Digitalization’s popularity is exponentially increasing. Soon, AI may be a part of every industry in the world. If educators modernize their lessons for this modern age, they can ensure today’s youth have a headstart once they reach the workforce. Artificial intelligence is rampant in schools, but education professionals aren’t teaching students the importance of ethical utilization. The problem is that it’s a complex topic, and educators simply don’t have the time or resources to tackle it. Artificial intelligence is rampant in schools, but education professionals aren’t teaching students the importance of ethical utilization. The problem is that it’s a complex topic, and educators simply don’t have the time or resources to tackle it. Where do they get the relevant materials? How do they fit it into their existing lesson plans? Here’s how questions like these are holding AI ethics classes back — and what decision-makers are doing to fix the problem. The Importance of Teaching AI Ethics in School AI ethics is a set of principles that govern the development, deployment and utilization of algorithm-based tools. They are meant to guide stakeholders, minimizing the damage such technologies can do to the environment, society or the economy. On a smaller scale, they focus on preventing bias, discrimination, misinformation and abuse. Teaching AI ethics in school may seem unnecessary, but its importance cannot be overstated. AI has already become a staple in many industries. According to a McKinsey & Company report, 72% of organizations use it for at least one business function. If its upward trajectory continues, its impact could rival that of computers, smartphones or the internet. 72% of organizations use it 72% of organizations use it Avoiding talking about AI in the classroom because of its newness needlessly repeats history. It is the same as years ago when students were told they wouldn’t always have a calculator in their back pocket right after the smartphone was invented. This technology’s popularity is growing in both personal and professional domains. Students must be prepared to use it responsibly when entering the workforce to ensure better outcomes for their industry and society. AI is powerful. Advanced algorithms can analyze massive datasets exponentially faster than humans. They excel at pattern recognition, trend prediction and content creation. Whether educators like it or not, AI will likely change the world. If they prepare their students accordingly, they can ensure that change is positive. Ethical Dilemmas Students Face When Using AI Typical ethical risks students face when using AI include technology abuse, harassment and plagiarism. The latter is one of the most common. Thanks to generative tools, academic dishonesty is easy to get away with. Google Trends reveals that searches for “AI essay writing” increased by a staggering 2,041% after ChatGPT’s release. increased by a staggering 2,041% increased by a staggering 2,041% Unfortunately, plagiarism isn’t the most severe offense. Lately, university, high school and even middle school students have been caught using generative tools to take part in deepfake-related sex crimes. In South Korea, one journalist uncovered one such group that had over 2,000 members. Many victims were believed to be under 16 — under Korea’s age of consent. uncovered one such group uncovered one such group Even if kids aren’t using regular photos of their classmates to create explicit material, AI use still revolves around consent. This concept revisits data privacy and security principles — all data end users enter is no longer private. The platform or service provider may use it to train their model or enhance their marketing strategies. Many of today’s students view generative and machine learning models as entertaining, harmless tools — even when they are actively harming others. Teachers have a unique opportunity to educate them early on about the consequences of such actions, stopping unacceptable behavior like this at the source. Is Ethical AI Use Being Taught in Schools Today? School board standards, classroom policies and state laws vary dramatically, so AI adoption has been erratic at best. This split between those who embrace it and those who don’t has stalled standardization. However, some are still taking a chance on ethics courses. Elementary School While various academics and industry experts argue ethics discussions should start in elementary school, relatively few institutions even use algorithm-based tools that early. There are virtually no public resources available for grade school administrators. Middle and High School Many middle and high schools aren’t discussing the intricacies of ethical AI. Instead, they are cracking down on AI use. Studies show that the accuracy of AI detection tools is typically about 50% , so most instances slip by unnoticed. As a result, kids often learn bad habits. is typically about 50% is typically about 50% Promisingly, some decision-makers have seen the need for AI ethics. For instance, California Governor Gavin Newsom signed Bill No. 2876 into law in September 2024. When curriculum frameworks are revised in January 2025, schools must incorporate AI literacy content — which guides critical and ethical use — into the classroom. signed Bill No. 2876 into law signed Bill No. 2876 into law Not all educators receive playbooks from experts. Some, like Jeff Simon, a mathematics teacher at Sage Creek High School, are navigating this new field on their own. Simon said he is trying to “teach the ethical use” of AI since it is  “just going to keep on growing.” trying to “teach the ethical use” trying to “teach the ethical use” College and University Most AI ethics classes are offered at higher education institutions because working those discussions into data science or computer engineering courses is easier. At the very least, many have developed policies for generative AI use. Reasons Why AI Ethics Isn’t Being Taught in School AI is not just present in education — it is thriving. For reference, in 2024, 86% of students admitted to using AI for schoolwork. Despite its popularity, many schools have no guiding principles on how to use these models ethically. 86% of students admitted 86% of students admitted Time is one of the main constraints — educators face the impossible task of doing more with less. They already spend 54 hours at work per week. How will they learn about AI and develop classroom policies that align with ethics in addition to teaching curriculum, grading papers and creating lesson plans? spend 54 hours at work spend 54 hours at work The field of AI is complex and rapidly evolving. Even with district-wide support, educational materials may quickly become outdated. Many schools still use decades-old textbooks to teach, so any solution would require extensive retrofitting. Moreover, how do they send home ethics homework? What would tests look like? How would they assign fair grades? Educators may be willing to embrace the concept of ethical AI classes if given the resources, but there’s no clear framework on how that could be done. Should they begin teaching it in elementary school? Will it replace a class, be offered as an elective or be integrated into the existing curriculum? There’s no one-size-fits-all solution. Many teachers have adopted “not in my classroom” policies in response to these concerns. As a result, students likely won’t learn how to use these tools the right way. Many will take whatever inaccurate, unregulated knowledge they cobble together from peers, parents and the internet as the truth — which may have far-reaching impacts. Addressing Concerns to Make AI Ethics Classes a Reality There are several ways education professionals can address why AI ethics isn’t being taught in schools. Place AI Ethics Wherever It Fits Integrating AI ethics into the classroom can mean creating a new elective or squeezing it in at the end of the period. Placing it where it naturally appears is best. For example, when students are assigned an eight-page essay, they can hear about responsible generative AI use. Teachers and administrators should adapt these new courses to students’ grade, technical expertise and knowledge levels to make the subject matter age-appropriate and engaging. Assignments should be relatively open-ended but follow a clear ethical framework. Tailor Subject Matter for End Users Typically, conversations about ethical AI focus on data privacy and output bias, centering around people who develop, deploy or manage these tools. End users interact with the user interface — not the backend — so their approach must differ slightly. Core principles should be accountability, value alignment, explainability, fairness and integrity. Pupils should learn critical thinking to combat bias, reasoning to prevent overreliance and objective analysis to mitigate the spread of misinformation. Model Responsible AI Use If state law or school board policies make teaching the ethics of AI in the classroom difficult, educators should take advantage of workarounds. Modeling responsible, honorable behavior goes a long way. Alternatively, they can invite a guest speaker. Facilitate In-Class Ethics Discussions Facilitating discussions, debates and group work in class lets high school teachers and college professors act as moderators. Posing questions about data de-identification, copyright infringement and summarization as they relate to AI can initiate productive conversations. Modern Lessons for a Modern Age Digitalization’s popularity is exponentially increasing. Soon, AI may be a part of every industry in the world. If educators modernize their lessons for this modern age, they can ensure today’s youth have a headstart once they reach the workforce.

Why Aren't We Teaching AI Ethics in Schools?

Waarom onderrig ons nie KI-etiek in skole nie?

لماذا لا نقوم بتدريس أخلاقيات الذكاء الاصطناعي في المدارس؟

¿Kunatsa Yatiqañ Utanakan jan AI Ética Yatichktanti?

Per què no estem ensenyant l'ètica de la IA a les escoles?

Hvorfor underviser vi ikke i AI-etik i skolerne?

¿Por qué no enseñamos la ética de la IA en las escuelas?

Pourquoi n’enseignons-nous pas l’éthique de l’IA dans les écoles ?

なぜ学校でAI倫理を教えないのか?

Почему мы не преподаем этику ИИ в школах?

Neden Okullarda Yapay Zeka Etiğini Öğretmiyoruz?

Ha Yini Hi Nga Dyondzisi Mahanyelo Ya AI eSwikolweni?

Tại sao chúng ta không dạy đạo đức AI ở trường học?

Using Cursor Can Feel Like Magic—Until It Doesn’t AI-generated code can feel revolutionary—until it fails you. Why does this happen? Often, the issue isn't with the AI itself, but with the inputs we provide. AI thrives on precise and comprehensive instructions. If your prompts are unclear or lack essential context, the fault lies not with the AI but with the user. The Developer’s Dilemma: Context Is Everything Imagine this scenario: you submit a prompt, and Cursor, the AI code editor, delivers what looks like a flawless solution. But then you run the code, and nothing works. Why? Because Cursor lacked the necessary context to understand your unique environment. Here’s what typically goes wrong: Mismatch in Operating Systems: You’re on Linux, but Cursor assumes macOS.
Conflicting Ports or Docker Errors: Cursor generates code oblivious to the state of your running containers.
Python Environment Issues: Your specific dependencies or versions don’t align with the generated code. These are not Cursor’s failures—they’re gaps in the context we provide. Without environmental awareness, even the smartest AI can only guess at solutions. The Solution: Introducing CursorBoost To bridge this gap, I created CursorBoost—a lightweight agent designed to ensure AI has the context it needs to work effectively. It captures critical details about your system in real-time and integrates them seamlessly into your AI interactions. Here’s how CursorBoost transforms your experience: Environment Monitoring: CursorBoost actively tracks vital system details, including:

Operating System
Open Ports
Docker Logs
Python Versions


Dynamic Updates: The agent writes this information into a .cursorrules file—a centralized snapshot of your system’s state.
Enhanced Prompts: By integrating this context, CursorBoost ensures that the AI generates solutions tailored to your specific environment. Without CursorBoost, the AI might suggest using port 8080—only to clash with an active process. With CursorBoost, it knows which ports are open and offers an alternative, saving you from debugging frustration. Why CursorBoost Matters CursorBoost takes the guesswork out of AI-assisted development, delivering practical benefits: Accurate Solutions: Code that aligns with your system from the start.
Time Saved: Fewer iterations and clarifications.
Reduced Frustration: Debugging becomes smoother when the AI understands your setup. This isn’t just about convenience—it’s about making AI truly work for you. Real-Life Success: CursorBoost at the Nvidia & Vercel Hackathon CursorBoost was born out of necessity during an Nvidia and Vercel hackathon. The problem was personal—AI-generated solutions that didn’t align with my workflow. By solving it, I not only saved hours of development time but also won an RTX 4080 GPU signed by Jensen Huang. However, the real prize wasn’t the hardware—it was seeing how much more effective AI could be with the right context. CursorBoost turned my daily frustrations into a scalable solution for developers everywhere. The Golden Rule of Prompt Engineering: Context Is Key The quality of AI output is directly proportional to the clarity of your input. CursorBoost ensures that your AI assistant always has the context it needs to succeed. With better tools, we can spend less time troubleshooting and more time building. Let’s Build Smarter Tools—Together The future of coding lies in collaboration between developers and AI. Tools like CursorBoost bridge the gap, making AI more effective and workflows more efficient. If you’re passionate about improving developer tools or workflows, let’s connect. Together, we can make AI an even more powerful ally in coding. Check out CursorBoost on GitHub: https://github.com/grp06/cursor-boost Using Cursor Can Feel Like Magic—Until It Doesn’t AI-generated code can feel revolutionary—until it fails you. Why does this happen? Often, the issue isn't with the AI itself, but with the inputs we provide. AI thrives on precise and comprehensive instructions. If your prompts are unclear or lack essential context, the fault lies not with the AI but with the user. The Developer’s Dilemma: Context Is Everything Imagine this scenario: you submit a prompt, and Cursor, the AI code editor, delivers what looks like a flawless solution. But then you run the code, and nothing works. Why? Because Cursor lacked the necessary context to understand your unique environment. Here’s what typically goes wrong: Mismatch in Operating Systems: You’re on Linux, but Cursor assumes macOS. Conflicting Ports or Docker Errors: Cursor generates code oblivious to the state of your running containers. Python Environment Issues: Your specific dependencies or versions don’t align with the generated code. Mismatch in Operating Systems : You’re on Linux, but Cursor assumes macOS. Mismatch in Operating Systems Conflicting Ports or Docker Errors : Cursor generates code oblivious to the state of your running containers. Conflicting Ports or Docker Errors Python Environment Issues : Your specific dependencies or versions don’t align with the generated code. Python Environment Issues These are not Cursor’s failures—they’re gaps in the context we provide. Without environmental awareness, even the smartest AI can only guess at solutions. The Solution: Introducing CursorBoost To bridge this gap, I created CursorBoost —a lightweight agent designed to ensure AI has the context it needs to work effectively. It captures critical details about your system in real-time and integrates them seamlessly into your AI interactions. CursorBoost Here’s how CursorBoost transforms your experience: Environment Monitoring: CursorBoost actively tracks vital system details, including:

Operating System
Open Ports
Docker Logs
Python Versions Dynamic Updates: The agent writes this information into a .cursorrules file—a centralized snapshot of your system’s state. Enhanced Prompts: By integrating this context, CursorBoost ensures that the AI generates solutions tailored to your specific environment. Environment Monitoring : CursorBoost actively tracks vital system details, including: Operating System
Open Ports
Docker Logs
Python Versions Environment Monitoring Operating System Open Ports Docker Logs Python Versions Operating System Open Ports Docker Logs Python Versions Dynamic Updates : The agent writes this information into a .cursorrules file—a centralized snapshot of your system’s state. Dynamic Updates .cursorrules Enhanced Prompts : By integrating this context, CursorBoost ensures that the AI generates solutions tailored to your specific environment. Enhanced Prompts Without CursorBoost, the AI might suggest using port 8080—only to clash with an active process. With CursorBoost, it knows which ports are open and offers an alternative, saving you from debugging frustration. Why CursorBoost Matters CursorBoost takes the guesswork out of AI-assisted development, delivering practical benefits: Accurate Solutions: Code that aligns with your system from the start. Time Saved: Fewer iterations and clarifications. Reduced Frustration: Debugging becomes smoother when the AI understands your setup. Accurate Solutions : Code that aligns with your system from the start. Accurate Solutions Time Saved : Fewer iterations and clarifications. Time Saved Reduced Frustration : Debugging becomes smoother when the AI understands your setup. Reduced Frustration This isn’t just about convenience—it’s about making AI truly work for you. Real-Life Success: CursorBoost at the Nvidia & Vercel Hackathon CursorBoost was born out of necessity during an Nvidia and Vercel hackathon . The problem was personal—AI-generated solutions that didn’t align with my workflow. By solving it, I not only saved hours of development time but also won an RTX 4080 GPU signed by Jensen Huang . Nvidia and Vercel hackathon RTX 4080 GPU signed by Jensen Huang However, the real prize wasn’t the hardware—it was seeing how much more effective AI could be with the right context. CursorBoost turned my daily frustrations into a scalable solution for developers everywhere. The Golden Rule of Prompt Engineering: Context Is Key The quality of AI output is directly proportional to the clarity of your input. CursorBoost ensures that your AI assistant always has the context it needs to succeed. With better tools, we can spend less time troubleshooting and more time building. Let’s Build Smarter Tools—Together The future of coding lies in collaboration between developers and AI. Tools like CursorBoost bridge the gap, making AI more effective and workflows more efficient. If you’re passionate about improving developer tools or workflows, let’s connect. Together, we can make AI an even more powerful ally in coding. Check out CursorBoost on GitHub: https://github.com/grp06/cursor-boost Check out CursorBoost on GitHub: https://github.com/grp06/cursor-boost https://github.com/grp06/cursor-boost

Bad Prompts, Bad Results: Why Every LLM Failure Is Your Fault

Дрэнныя падказкі, дрэнныя вынікі: чаму кожны збой LLM - ваша віна

Лоши подкани, лоши резултати: Защо всеки провал на LLM е по ваша вина

Loši upiti, loši rezultati: Zašto je svaki neuspjeh LLM vaša greška

Malos avisos, malos resultados: por qué cada fracaso en un LLM es culpa tuya

درخواست‌های بد، نتایج بد: چرا هر شکست LLM تقصیر شماست

Վատ հուշումներ, վատ արդյունքներ. ինչու է LLM-ի յուրաքանչյուր ձախողում ձեր մեղքը

הנחיות רעות, תוצאות רעות: מדוע כל כשל ב-LLM הוא באשמתך

悪いプロンプト、悪い結果: すべての LLM 不合格はあなたのせいである理由

Жаман сунуштар, жаман натыйжалар: Эмне үчүн ар бир LLM ийгиликсиздиги сиздин күнөөңүз

Maoni Mbaya, Matokeo Mabaya: Kwa Nini Kila Kushindwa kwa LLM ni Kosa Lako

Switsundzuxo Swo Biha, Vuyelo Byo Biha: Ha Yini Ku Hluleka Hinkwako Ka LLM Ku Ri Xihoxo Xa Wena

糟糕的提示，糟糕的结果：为什么每次法学硕士失败都是你的错

Artificial Minds, Human Consequences: Explaining AI’s Impact on Our Education, Cognition, and More

AI is the buzzword everyone’s throwing around, but does it really live up to the hype? The truth is that AI is just another tool in the digital toolbox. It’s not the AI itself that changes the game—it’s how we use it to solve complex problems, scale human effort, and empower creativity. The real innovation comes from people—the ones asking the right questions and pushing boundaries with AI. Want to disrupt the status quo? Focus on human insights, not algorithms. AI Is Not a Silver Bullet We’ve seen this pattern before: as soon as a technology becomes mainstream, it ceases to be a differentiator. Once heralded as the future, AI is now on the same path. But here’s where many go wrong: They think integrating AI into their products will instantly catapult them to success. The reality? If everyone has access to the same technology, then where’s the competitive edge? Instead of treating AI like a magical solution, recognize it for what it is—a highly capable assistant. It can analyze data at unprecedented speeds, automate mundane tasks, and uncover hidden patterns. But it won’t conjure up a winning strategy or envision breakthrough concepts. That part? Still very much human. What Actually Drives Innovation? History’s greatest technological leaps weren’t driven by technology alone. It’s always been about contextual adaptation—using tools to transform industries, create new business models, and change human behavior. The smartphone revolution wasn’t just about hardware. It was about how we reimagined communication, commerce, and connectivity. AI is no different. Here’s what truly matters: Strategic implementation: How you align AI with business goals is what counts. Are you using AI to simply replace existing processes, or are you enhancing your ability to make better decisions? Problem framing: AI’s effectiveness hinges on the quality of the problems it’s tasked to solve. Knowing what to ask is more powerful than how many data points you can feed it. People and Culture: Organizations that innovate successfully focus on upskilling teams, nurturing creative thinking, and fostering a culture of curiosity. The Human Factor: Where True Value Lies AI might be able to beat grandmasters at chess, but it still can’t come up with why you should play chess in the first place. It’s humans who define purpose, context, and ethical considerations. The companies that thrive will be those that invest in people—hiring diverse thinkers, enabling cross-disciplinary collaboration, and empowering employees to use AI as a launchpad, not a crutch. The Bottom Line: Human-Driven AI Wins So, is AI useless? Far from it. But the obsession with AI as a standalone solution is misplaced. The true winners will be those who blend human expertise and AI capabilities to unlock new opportunities. AI isn’t the star of the show; it’s a backstage crew member making sure the spotlight shines where it should. In other words, focus less on the technology itself, and more on the humans harnessing its potential. That’s where the real magic happens. AI is the buzzword everyone’s throwing around, but does it really live up to the hype? The truth is that AI is just another tool in the digital toolbox. It’s not the AI itself that changes the game—it’s how we use it to solve complex problems, scale human effort, and empower creativity. The real innovation comes from people —the ones asking the right questions and pushing boundaries with AI. Want to disrupt the status quo? Focus on human insights , not algorithms. people human insights AI Is Not a Silver Bullet AI Is Not a Silver Bullet We’ve seen this pattern before: as soon as a technology becomes mainstream, it ceases to be a differentiator. Once heralded as the future, AI is now on the same path. But here’s where many go wrong: They think integrating AI into their products will instantly catapult them to success. The reality? If everyone has access to the same technology, then where’s the competitive edge? Instead of treating AI like a magical solution, recognize it for what it is—a highly capable assistant . It can analyze data at unprecedented speeds, automate mundane tasks, and uncover hidden patterns. But it won’t conjure up a winning strategy or envision breakthrough concepts. That part? Still very much human. assistant What Actually Drives Innovation? What Actually Drives Innovation? History’s greatest technological leaps weren’t driven by technology alone. It’s always been about contextual adaptation —using tools to transform industries, create new business models, and change human behavior. The smartphone revolution wasn’t just about hardware. It was about how we reimagined communication, commerce, and connectivity. AI is no different. contextual adaptation Here’s what truly matters: Strategic implementation: How you align AI with business goals is what counts. Are you using AI to simply replace existing processes, or are you enhancing your ability to make better decisions? Strategic implementation: How you align AI with business goals is what counts. Are you using AI to simply replace existing processes, or are you enhancing your ability to make better decisions? Strategic implementation: Problem framing: AI’s effectiveness hinges on the quality of the problems it’s tasked to solve. Knowing what to ask is more powerful than how many data points you can feed it. Problem framing: AI’s effectiveness hinges on the quality of the problems it’s tasked to solve. Knowing what to ask is more powerful than how many data points you can feed it. Problem framing: what People and Culture: Organizations that innovate successfully focus on upskilling teams, nurturing creative thinking, and fostering a culture of curiosity. People and Culture: Organizations that innovate successfully focus on upskilling teams, nurturing creative thinking, and fostering a culture of curiosity. People and Culture: The Human Factor: Where True Value Lies The Human Factor: Where True Value Lies AI might be able to beat grandmasters at chess, but it still can’t come up with why you should play chess in the first place. It’s humans who define purpose, context, and ethical considerations. The companies that thrive will be those that invest in people —hiring diverse thinkers, enabling cross-disciplinary collaboration, and empowering employees to use AI as a launchpad, not a crutch. why people The Bottom Line: Human-Driven AI Wins The Bottom Line: Human-Driven AI Wins So, is AI useless? Far from it. But the obsession with AI as a standalone solution is misplaced. The true winners will be those who blend human expertise and AI capabilities to unlock new opportunities. AI isn’t the star of the show; it’s a backstage crew member making sure the spotlight shines where it should. In other words, focus less on the technology itself, and more on the humans harnessing its potential. That’s where the real magic happens. humans

Why AI Doesn’t Matter (What Actually Does) 

Will Artificial Intelligence Take Over Humans?

አርቴፊሻል ኢንተለጀንስ ሰዎችን ይቆጣጠራሉ?

¿La Inteligencia Artificial superará a los humanos?

L'intelligenza artificiale prenderà il sopravvento sugli esseri umani?

人工知能は人間を支配するのか？

Жасанды интеллект адамдарды жаулап ала ма?

Mayele ya mayele ekozwa bato?

¿Inteligencia Artificialchu Runakunata hap’ikapunqa?

Va prelua inteligența artificială pe oameni?

Оё зеҳни сунъӣ одамонро тасарруф мекунад?

ปัญญาประดิษฐ์จะเข้ามาแทนที่มนุษย์ได้หรือไม่?

Maaagaw ba ng Artipisyal na Katalinuhan ang mga Tao?

人工智能会取代人类吗？

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